Data driven high quantum yield halide perovskite phosphors design and fabrication

IF 21.1 1区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials Today Pub Date : 2024-05-01 DOI:10.1016/j.mattod.2024.02.002

Haoxin Mai , Xiaoming Wen , Xuying Li , Nethmi S.L. Dissanayake , Xueqian Sun , Yuerui Lu , Tu C. Le , Salvy P. Russo , Dehong Chen , David A. Winkler , Rachel A. Caruso

{"title":"Data driven high quantum yield halide perovskite phosphors design and fabrication","authors":"Haoxin Mai , Xiaoming Wen , Xuying Li , Nethmi S.L. Dissanayake , Xueqian Sun , Yuerui Lu , Tu C. Le , Salvy P. Russo , Dehong Chen , David A. Winkler , Rachel A. Caruso","doi":"10.1016/j.mattod.2024.02.002","DOIUrl":null,"url":null,"abstract":"<div>The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs4Zn(Bi0.85Sb0.15)2Cl12:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.</div>","PeriodicalId":387,"journal":{"name":"Materials Today","volume":"74 ","pages":"Pages 12-21"},"PeriodicalIF":21.1000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S136970212400021X/pdfft?md5=53d18db93e44026d950fab5dd4c6fa7f&pid=1-s2.0-S136970212400021X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136970212400021X","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

The outstanding emission of halide perovskites make them ideal candidates for white emission light-emitting diodes (LEDs) for lighting applications. However, many perovskites contain toxic or scarce elements and have unsatisfactory stability. Here, we report a target-driven approach, based on active learning (AL) techniques, to discover halide perovskites suitable for commercial LED applications. Based on the similarity between halide and oxide perovskites, a model trained on an oxide perovskite dataset plus six AL-selected halide perovskites exhibited excellent performance for photoluminescence quantum yield (PLQY) predictions of oxide and halide perovskites. The model proposed a strong relationship between ionic radii and PLQY, postulated to be due to the self-trap excitons derived from the Jahn-Teller deformation. A novel halide perovskite phosphor, Cs₄Zn(Bi_0.85Sb_0.15)₂Cl₁₂:0.01Mn, was designed and synthesized with the aid of the model. It exhibited an 88 % PLQY and outstanding thermal and luminescent stability. A simple white LED was fabricated from this material, exemplifying its commercial potential. This study demonstrates how machine learning techniques can accelerate discovery of next-generation phosphors for high performance single emitter-based white-light emitting devices.

Abstract Image

查看原文本刊更多论文

数据驱动的高量子产率卤化物包晶荧光粉设计与制造

卤化物过氧化物具有出色的发射性能，是照明用白光发光二极管（LED）的理想候选材料。然而，许多过氧化物晶石都含有有毒或稀缺的元素，稳定性也不尽如人意。在此，我们报告了一种基于主动学习（AL）技术的目标驱动方法，以发现适合商业 LED 应用的卤化物包晶。基于卤化物包晶石和氧化物包晶石之间的相似性，在氧化物包晶石数据集和六种 AL 挑选出的卤化物包晶石上训练出的模型在预测氧化物和卤化物包晶石的光致发光量子产率 (PLQY) 方面表现出色。该模型提出了离子半径与 PLQY 之间的密切关系，并推测这是由于扬-泰勒变形产生的自捕获激子所致。在该模型的帮助下，设计并合成了一种新型卤化物包晶荧光粉 Cs4Zn(Bi0.85Sb0.15)2Cl12:0.01Mn。它显示出 88% 的 PLQY 以及出色的热稳定性和发光稳定性。利用这种材料制造出了一种简单的白光 LED，充分体现了其商业潜力。这项研究展示了机器学习技术如何加速发现下一代荧光粉，用于基于单发射极的高性能白光发光器件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Materials Today 工程技术-材料科学：综合

CiteScore

36.30

自引率

1.20%

发文量

237

审稿时长

23 days

期刊介绍： Materials Today is the leading journal in the Materials Today family, focusing on the latest and most impactful work in the materials science community. With a reputation for excellence in news and reviews, the journal has now expanded its coverage to include original research and aims to be at the forefront of the field. We welcome comprehensive articles, short communications, and review articles from established leaders in the rapidly evolving fields of materials science and related disciplines. We strive to provide authors with rigorous peer review, fast publication, and maximum exposure for their work. While we only accept the most significant manuscripts, our speedy evaluation process ensures that there are no unnecessary publication delays.